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Development and validation of a genomic mutation signature to predict response to PD-1 inhibitors in non-squamous NSCLC: a multicohort study
BACKGROUND: Genetic variations of some driver genes in non-small cell lung cancer (NSCLC) had shown potential impact on immune microenvironment and associated with response or resistance to programmed cell death protein 1 (PD-1) blockade immunotherapy. We therefore undertook an exploratory analysis...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328897/ https://www.ncbi.nlm.nih.gov/pubmed/32606052 http://dx.doi.org/10.1136/jitc-2019-000381 |
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author | Bai, Xue Wu, De-Hua Ma, Si-Cong Wang, Jian Tang, Xin-Ran Kang, Shuai Fu, Qiang John Cao, Chuan-Hui Luo, He-San Chen, Yu-Han Zhu, Hong-Bo Yan, Hong-Hong Wu, Yi-Long Dong, Zhong-Yi |
author_facet | Bai, Xue Wu, De-Hua Ma, Si-Cong Wang, Jian Tang, Xin-Ran Kang, Shuai Fu, Qiang John Cao, Chuan-Hui Luo, He-San Chen, Yu-Han Zhu, Hong-Bo Yan, Hong-Hong Wu, Yi-Long Dong, Zhong-Yi |
author_sort | Bai, Xue |
collection | PubMed |
description | BACKGROUND: Genetic variations of some driver genes in non-small cell lung cancer (NSCLC) had shown potential impact on immune microenvironment and associated with response or resistance to programmed cell death protein 1 (PD-1) blockade immunotherapy. We therefore undertook an exploratory analysis to develop a genomic mutation signature (GMS) and predict the response to anti-PD-(L)1 therapy. METHODS: In this multicohort analysis, 316 patients with non-squamous NSCLC treated with anti-PD-(L)1 from three independent cohorts were included in our study. Tumor samples from the patients were molecularly profiled by MSK-IMPACT or whole exome sequencing. We developed a risk model named GMS based on the MSK training cohort (n=123). The predictive model was first validated in the separate internal MSK cohort (n=82) and then validated in an external cohort containing 111 patients from previously published clinical trials. RESULTS: A GMS risk model consisting of eight genes (TP53, KRAS, STK11, EGFR, PTPRD, KMT2C, SMAD4, and HGF) was generated to classify patients into high and low GMS groups in the training cohort. Patients with high GMS in the training cohort had longer progression-free survival (hazard ratio (HR) 0.41, 0.28–0.61, p<0.0001) and overall survival (HR 0.53, 0.32–0.89, p=0.0275) compared with low GMS. We noted equivalent findings in the internal validation cohort and in the external validation cohort. The GMS was demonstrated as an independent predictive factor for anti-PD-(L)1 therapy comparing with tumor mutational burden. Meanwhile, GMS showed undifferentiated predictive value in patients with different clinicopathological features. Notably, both GMS and PD-L1 were independent predictors and demonstrated poorly correlated; inclusion of PD-L1 with GMS further improved the predictive capacity for PD-1 blockade immunotherapy. CONCLUSIONS: Our study highlights the potential predictive value of GMS for immunotherapeutic benefit in non-squamous NSCLC. Besides, the combination of GMS and PD-L1 may serve as an optimal partner in guiding treatment decisions for anti-PD-(L)1 based therapy. |
format | Online Article Text |
id | pubmed-7328897 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-73288972020-07-02 Development and validation of a genomic mutation signature to predict response to PD-1 inhibitors in non-squamous NSCLC: a multicohort study Bai, Xue Wu, De-Hua Ma, Si-Cong Wang, Jian Tang, Xin-Ran Kang, Shuai Fu, Qiang John Cao, Chuan-Hui Luo, He-San Chen, Yu-Han Zhu, Hong-Bo Yan, Hong-Hong Wu, Yi-Long Dong, Zhong-Yi J Immunother Cancer Immunotherapy Biomarkers BACKGROUND: Genetic variations of some driver genes in non-small cell lung cancer (NSCLC) had shown potential impact on immune microenvironment and associated with response or resistance to programmed cell death protein 1 (PD-1) blockade immunotherapy. We therefore undertook an exploratory analysis to develop a genomic mutation signature (GMS) and predict the response to anti-PD-(L)1 therapy. METHODS: In this multicohort analysis, 316 patients with non-squamous NSCLC treated with anti-PD-(L)1 from three independent cohorts were included in our study. Tumor samples from the patients were molecularly profiled by MSK-IMPACT or whole exome sequencing. We developed a risk model named GMS based on the MSK training cohort (n=123). The predictive model was first validated in the separate internal MSK cohort (n=82) and then validated in an external cohort containing 111 patients from previously published clinical trials. RESULTS: A GMS risk model consisting of eight genes (TP53, KRAS, STK11, EGFR, PTPRD, KMT2C, SMAD4, and HGF) was generated to classify patients into high and low GMS groups in the training cohort. Patients with high GMS in the training cohort had longer progression-free survival (hazard ratio (HR) 0.41, 0.28–0.61, p<0.0001) and overall survival (HR 0.53, 0.32–0.89, p=0.0275) compared with low GMS. We noted equivalent findings in the internal validation cohort and in the external validation cohort. The GMS was demonstrated as an independent predictive factor for anti-PD-(L)1 therapy comparing with tumor mutational burden. Meanwhile, GMS showed undifferentiated predictive value in patients with different clinicopathological features. Notably, both GMS and PD-L1 were independent predictors and demonstrated poorly correlated; inclusion of PD-L1 with GMS further improved the predictive capacity for PD-1 blockade immunotherapy. CONCLUSIONS: Our study highlights the potential predictive value of GMS for immunotherapeutic benefit in non-squamous NSCLC. Besides, the combination of GMS and PD-L1 may serve as an optimal partner in guiding treatment decisions for anti-PD-(L)1 based therapy. BMJ Publishing Group 2020-06-30 /pmc/articles/PMC7328897/ /pubmed/32606052 http://dx.doi.org/10.1136/jitc-2019-000381 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Immunotherapy Biomarkers Bai, Xue Wu, De-Hua Ma, Si-Cong Wang, Jian Tang, Xin-Ran Kang, Shuai Fu, Qiang John Cao, Chuan-Hui Luo, He-San Chen, Yu-Han Zhu, Hong-Bo Yan, Hong-Hong Wu, Yi-Long Dong, Zhong-Yi Development and validation of a genomic mutation signature to predict response to PD-1 inhibitors in non-squamous NSCLC: a multicohort study |
title | Development and validation of a genomic mutation signature to predict response to PD-1 inhibitors in non-squamous NSCLC: a multicohort study |
title_full | Development and validation of a genomic mutation signature to predict response to PD-1 inhibitors in non-squamous NSCLC: a multicohort study |
title_fullStr | Development and validation of a genomic mutation signature to predict response to PD-1 inhibitors in non-squamous NSCLC: a multicohort study |
title_full_unstemmed | Development and validation of a genomic mutation signature to predict response to PD-1 inhibitors in non-squamous NSCLC: a multicohort study |
title_short | Development and validation of a genomic mutation signature to predict response to PD-1 inhibitors in non-squamous NSCLC: a multicohort study |
title_sort | development and validation of a genomic mutation signature to predict response to pd-1 inhibitors in non-squamous nsclc: a multicohort study |
topic | Immunotherapy Biomarkers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328897/ https://www.ncbi.nlm.nih.gov/pubmed/32606052 http://dx.doi.org/10.1136/jitc-2019-000381 |
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